the need for speed: a overview technical and clinical ... · pmri: snr properties & artifacts...
TRANSCRIPT
8/1/2011
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The Need for Speed: A
Technical and Clinical Primer
for Parallel MR Imaging
Nathan Yanasak, Ph.D.
Chair, AAPM TG118
Assistant Professor
Department of Radiology
Director, Core Imaging Facility for Small Animals (CIFSA)
Georgia Health Sciences University
• General Description of Parallel
Imaging
• Applications of Parallel Imaging
• pMRI Considerations
• Quality Control
Overview
• Uses spatial information obtained from arrays of RF coils sampling data in parallel
• Information is used to perform some portionof spatial encoding usually done by gradient fields (typically phase-encoding gradient)
• Speeds up MRI acquisition times
– without needing faster-switching gradients
– without additional RF power deposited (key for higher field MR)
What is pMRI?Speed: less phase encodes = smaller
FOV (with same resolution)
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aliasing
Smaller FOV
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Uneven SNR throughout volume, but …
• Very high SNR at edge
• Lower SNR in middle
• SNR in middle is generally better than comparable volume coil.
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Phased-Array of Coil Elements SNR: Surface Coil vs. Volume CoilSurface Coil (single element) Volume Coil
Non-uniform SNR Uniform SNR
Great SNR up close Average SNR
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SIG
NA
L-T
O-N
OIS
E R
AT
IO (A
rbitra
ry U
nits)
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5
4
3
2
1
05 1510
a
b
c
d
body
head
(a) 8 cm dia. Surface coil
(b) 10 cm dia. Surface coil
(c) 14 cm dia. Surface coil
(d) Head coil
DEPTH (in cm)
Multichannel Coils
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12-channel head coil Multi-element full body coil
Use of Phased Array Coil in
Parallel Imaging
Conventional use of phased-array
(unaliased)Parallel reconstruction of data
(aliased)
Spatial sensitivity varies for each element can
use this in conjunction with undersampling.
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Coil Sensitivity Profiles
• Different approaches to solving the inverse
problem that recovers spatial information.
• The key information always required to solve this
problem is information on the spatial distribution
of the RF coils’ sensitivity.
• How you collect and use this information
different methods.
Sensitivity Map
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The spatial sensitivity of each coil element = sensitivity map.
A calibration scan is usually required to calculate this.
s1
Totals2
Using Coil Sensitivity to Un-alias
an Image: An Example
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Coil Locations and Sensitivity
Maps
Object
being
imaged
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Using Coil Sensitivity to Un-alias an
Image
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Two Parallel Approaches
• Image based: Reconstruct
images from each element,
then untangle (SENSE,
ASSET) (our demo)
• k-Space based: Untangle
data to create fully-filled k-
space, then reconstruct image
(SMASH, GRAPPA)
The Encoding Matrix
Sp: signal received by the coil, p.
j: proton density at the pixel index, j
Bpj: encoding function that connects the coil response
to the proton signal at a location.
In matrix notation: S = B
or inverting: = B-1S
Thus if B-1 can be calculated, can be determined.
j
jpjpBS
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Salias,1=B1,AIA + B1,BIB
A Simplistic SENSE
ExampleA
B
B
Salias,2=B2,AIA + B2,BIB
A
IA
IB
I2
I1s1
s2
k-Space Based pMRI
• Assumes spatial harmonics of phase-
encoding gradients can be omitted and
emulated by a linear combination of coil
sensitivities
• Coil sensitivity still required (measured
in some manner, and complex).
(from Sodickson, et al., MRM 41: 1009, 1999)
A Simplistic SMASH
Example
• Weighted linear combination of element responses are sensitive to different spatial scales (but…no recon yet).
A Simplistic SMASH
Example
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• Resultant combinations (spatial harmonics) allow for filling of all lines in a composite k-space.
A Simplistic SMASH
Example
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The black curves represent two of the effective sensitivities
using elements in this example.
Fundamental: CA (add signal for all three)
First Harmonic: CB (subtract middle signal)
The upper combination is sensitive
to these spatial variations:
…while the lower combination is
sensitive to these spatial variations:
A Simplistic SMASH Example
• Acquire reference lines (ACS lines) in k-space rather than whole coil sensitivity images (data from center of k-space acts like a sensitivity profile)
Example: GRAPPA (k-space based)
• Missing k-space lines are synthesized by fitting between reference data and nearest neighbor lines of data
• Fitting determines the weighting factors for generating missing lines for each coil
Auto-Calibration MethodsParallel Imaging (Technique
Pros/Cons)
Image-based reconstruction: More artifacts, but
easier to implement the sequence.
K-space based reconstruction: Depends more
strongly on coil design, less artifacts, but longer to
reconstruct.
Hybrids between both also exist...
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Parallel Imaging Flavors
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Name Acronym Method Manufacturer
SENSitivity Encoding SENSE Image-based,
reference scan
Philips
Array Spatial Sensitivity Encoding
Technique
ASSET Image-based,
reference scan
General Electric
Auto-calibrating Reconstruction for
Cartesian imaging
ARC Hybrid (image-
and k-space
based)
General Electric
integrated Parallel Acquisition Techniques iPAT Used by all
pMRI
Siemens
GeneRalized Auto-calibrating Partially
Parallel Acquisition
GRAPPA k-space based,
auto-calibrated
with reference
scan option
Siemens
modified SENSitivity Encoding mSENSE image based,
auto-calibrated
with reference
scan option
Siemens
SPEEDER image-based,
reference scan
Toshiba
Advantages/Uses of pMRI
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When Should You Use
Parallel MR Imaging?
• To reduce total scan time
• To speed up single-shot MRI methods
• To reduce TE on long echo-train methods
• To mitigate susceptibility, chemical shift and other artifacts (may cause others)
• To decrease RF heating (SAR) by minimizing number of RF pulses (B2)
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Use #1: Body Imaging
A: 22 sec acquisition w/ 15 sec breathhold
B: 11 sec acquisition w/ 11 sec breathhold + R=2
• To reduce total scan time (or eliminate breath holds)
• To decrease RF heating (SAR) by minimizing number of RF pulses
Margolis D et al. Top Magn Reson Imag 2004; 15: 197-206
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Use #2: Spinal Imaging
D: non-pMRI
E: R=2
• Image quality is of similar quality for ½ the scan time
Noebauer-Huhmann et al. Eur Radiol 2007; 17: 1147-1155
• Problem #1: Greater ETL lower SNR
• Problem #2: T2 relaxation during acquisition of ETL results in ―T2 blurring‖.
• Reductions in TEeff would be useful.
Use #3: Reduce T2 Blurring
(FSE)
Use #3: Reduce T2 Blurring
Augustine Me et al. Top Magn Reson Imag 2004; 15:207Glockner et all. RadioGraphics 2005; 25: 1279-97
Use #4: Susceptibility
Artifacts – Air Sinuses
• Regions of air/bone/soft tissue causes local gradients due to differences in magnetic field susceptibility
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Susceptibility Artifact Reduction
with Parallel Imaging
• Shortening TE helps (must have less phase encodes to do this).
• EPI-based sequences gain more in general (e.g., DWI, perfusion)
Top – normal acquisition,
Bottom – R=2 acceleration
―Turn Key‖ Parallel Imaging ?R=1 R=2.0 R=2.8 R=3.2 R=4.0
―Turn Key‖ Parallel Imaging ?R=1 R=2.0 R=2.8 R=3.2 R=4.0
Left: R~ 1.5; Right: non-pMRI with reduced FOV
• Improved spatial resolution for a given scan time.
Use #5: Contrast-enhanced
MR (MRA)
Wilson, et al. Top Magn Reson Imag. 2004; 15: 169-185
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Use #6: Cardiac Imaging
Balanced FFE MRI
A&B: 11 sec breath holds
C&D: 5 sec breath holds + R=2
Van den Brink, et al. Eur. J. Rad. 2003; 46: 3-27.
Drawbacks/Consideration of
pMRI:
SNR Properties & Artifacts
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SNR
SNR is a concern with pMRI for three reasons:
• Non-uniformity of signal (array coils)
• Non-uniformity of noise (pMRI)
• Lower signal from acceleration (pMRI)
Non-Uniformity of Noise
Larkman DJ et al. Magn Reson Med 2006; 55:153-160
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Key SNR Parameters
in Parallel Imaging
• SNR depends on number, size and orientation of the coil elements
• R: acceleration factor
• g: coil-dependent noise amplification factor
(non-uniformity that we observed)
Rg
SNRSNR
kji
norm
kjiPI
kji
,,
,,
,,
Key SNR Parameters
in Parallel Imaging
• SNR depends on number, size and orientation of the coil elements
• R: acceleration factor
• g: coil-dependent noise amplification factor
(non-uniformity that we observed)
)(),( 2
1
rSNR
SNRRRrg
PI
norm
SNR vs. Acceleration
Short-axis cardiac images – 32-channel coil – 1.5 T magnet
Reeder SB et al. MRM 54:748, 2005
g-Factor Calculated Maps
Reeder SB et al. MRM 54:748, 2005
R=2 R=2
R=7
R=3
R=4 R=5
R=6
R=3
R=4 R=5
R=6 R=7
R-L Phase Encoding A-P Phase Encoding
32-channel coil, 1.5 T magnet
• g-Factor changes with R
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Artifacts
Artifacts associated with pMRI may or may not
be subtle.
Similarities to conventional MRI artifacts
(aliasing, ghosting).
Important to prescribe the acquisition
properly, and to avoid movement.
What happens when the FOV is too small?
Artifact #1: Tissue Outside of FOV
(SENSE)—Wrap-around artifact
Normal FOV Smaller
FOV
Undersampling…
Center region in this example should be unaliased, for acceleration R=2.
Treated as non-aliased tissue during reconstruction.
Unalias with pMRI
SENSESmall FOVNormal FOV
Examples: Phantom and
Patient
• With SENSE-based technique, tissue outside of the FOV yields ―wrap-into‖ artifact
Goldfarb, JMagn Reson Imag. 2004
Artifact #2: Motion After Calibration
Scan (SENSE or GRAPPA)Calibration scan must accurately represent tissue position.
Small
displacementMedium
displacement
Large
displacement
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Affected by FOV choice as well.
Small FOV Large FOV
Not aliasing, folks!
Artifact #2: Motion After Calibration
Scan (SENSE or GRAPPA)
Clinical Artifact Examples
Pseudo-‖failure‖ of fat sat: Patient moved between reference and
SENSE scans IAC structure ghosting3D artifact: faint ghost near the middle of FOV that resembles structures located at
the edges of scanned volume (nose, ear).
Clinical Artifact Examples
Thin, bright structures in the periphery of sensitivity map—mismatch
between sensitivity and anatomy.
Appearance of traditional artifacts may be modified by pMRI
Susceptibility (artifact not perfectly represented on sensitivity
map)
simulation phantom
pMRI and Traditional Artifacts
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Quality Control
SNR=SignalLarge ROI /NoiseSmall ROI
ACR QC vs. pMRI QC
ACR approach to SNR, Uniformity
(using volume coil, signal and noise
are fairly uniform in many cases)
U=1- |maxROI-minROI|/|maxROI+minROI|
ACR QC vs. pMRI QC
In pMRI, signal is not uniform (array
coils), and noise is not uniform (g-
factor).
SNR=mean(Ij,1+Ij,2)ROI /[sqrt(2Ij,1-Ij,2)ROI]NAAD=1- 1/N × (Ij - <I>)/<I>
TG118
Preliminary
Results: SNR
SNR
Update: TG118 has
focused on differences in
SNR, uniformity as
acceleration changes.
With SNR ROI in center,
will artifact perturb
measurement?
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TG118 Preliminary
Results:
Uniformity
Uniformity
Update: TG118 has
focused on differences in
SNR, uniformity as
acceleration changes.
Is more sensitivity good?
Thoughts from TG118 on
pMRI QA
• One must always remember that metrics and system performance can be interconnected (e.g., SNR vs. uniformity vs. artifacts)
• Not clear that any uniformity measure can satisfy all three criteria below
• easy to measure
• Sensitive to aberrant non-uniformity
• Insensitive to coil-inherent non-uniformity
Acceleration > 1D
2D SENSE reconstruction (2X in L-R and 2X in A-P) from an 8-channel head array coil conjugated gradient iterative solver after 10 iterations .
http://www.nmr.mgh.harvard.edu/~fhlin/tool_sense.htm
2D SENSE (with 3DFT MRI)
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Undersampling in the Temporal
Domain
• Parallel imaging in the temporal
domain: Time Auto-Calibrated
GRAPPA (TGRAPPA); TSENSE
• Self-Calibrating Non-Cartesian SENSE
TSENSE
2D Cine TrueFISP
Peter Kellman, NHLBI, and Al Zhang, Siemens R&D, Chicago
36 msec true temporal resolution, 144 x 256 matrix
R = 2
6 heartbeatsR = 3
4 heartbeats
R = 4
3 heartbeats
• pMRI with massively parallel arrays
• Transmit parallel imaging
Future Directions Importance of pMRI
• Increases MR imaging speed
• Is applicable to all MRI sequences
• Is complimentary to all existing MRI acceleration methods
• Can often reduce artifacts
• Alters SNR in MR images
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Application of pMRI
• pMRI offers the promise of high resolution MR imaging at speeds as fast as MSCT
• Applications of parallel imaging include FSE, cardiac MR, diffusion and perfusion EPI brain imaging methods, 3D FT MRI (and MRA).
• Parallel imaging is tool for managing RF heating in the body at 3T and higher field strengths
• Parallel imaging and dedicated RF coil design are enabling technologies for high Bo MRI
Acknowledgments
• Current and Past Members of TG118
• Jason Stafford, Lisa Lemen, Max Amurao, Geoff Clarke, Ron Price, Ishtiaq Bercha, Michael Steckner
• Frank Goerner (UTHSCSA)
• Ed Jackson (MDA), Lawrence Wald (MGH), Jerry Allison (MCG)
• 64 strip
detectors used
to image a test
object with a
single readout
(MacDougal and
Wright, 2005).
The Future of the
Future?